Left ventricular hypertrophy (LVH) caused by high blood pressure is linked to increased mortality and arrhythmia risk. This study aimed to evaluate arrhythmia in hypertensive patients due to left ventricular hypertrophy (LVH). A cross-sectional study was performed, assessing participants' blood pressure, echocardiography and electrocardiography, and Holter monitoring in certain cases. There were 300 hypertensive patients >18 years attending the cardiology unit of Baghdad medical city. The study was conducted between January–June 2022. The electrocardiograms at rest for 300 adults with hypertension were investigated. 130 (43.5%) were females, and 170 (56.5%) were males. The mean age of participants was 58 years. Forty-nine (16.3

Background: Normal thyroid function is essential for neonatal growth and brain development. In a newborn infant with severe disease, endocrine regulation of hormones can be affected by abnormal metabolism. The assessment of thyroid parameters results in the recognition of a dysfunction and its association with disease severity. Objective: This study aimed to assess thyroid function profiles in critically ill neonates in the neonatal intensive care unit (NICU) compared with healthy controls. Additionally, we aimed to detect the presence of TD and its possible association with critical illness. Methods: A case-control study was performed in 100 neonates, comprising 50 sick neonates and 50 healthy controls. We measured thyroid functio

A new and hybrid deep learning-based approach for diagnosing faults in electric vehicle (EV) drive motors is proposed in this article. This article presents a new and hybrid deep learning-based method of diagnosing faults in the drive motors of electric vehicles (EV). In contrast to standard CNNLSTM approaches that depend on SoftMax classification, the introduced framework combines a Random Forest (RF) classifier to enhance the generalization, interpretability, and robustness of fault prediction. Furthermore meant for use on edge computing equipment with IoT integration, the design allows for real-time monitoring in resource-limited settings. The introduced algorithm utilizes a Random Forest (RF) classifier for accurate fault classification

Psoriasis is a chronic autoimmune inflammatory skin condition characterized by uncontrolled keratinocyte proliferation and potential systemic manifestations. Its pathogenesis involves activation of both innate and adaptive immune responses, leading to an imbalance in inflammatory cytokines. Interleukin (IL)-12 and IL-23 are key cytokines in the pathophysiology of psoriasis and sustain chronic skin inflammation. Biologic therapies, such as ustekinumab (UST), have been developed to induce long-term remission in moderate-to-severe psoriasis. The objective of this study was to identify differences in serum levels of inflammatory biomarkers [erythrocyte sedimentation rate, high-sensitivity C-reactive protein (hs-CRP), IL-12, IL-17, IL-22, and IL

Background Due to the intermittent, nonlinear, and uncertain behavior of renewable energy sources (res) such as solar and wind, grid stability and reliability require very high forecasting and optimization skills as widely reported in the literature. Traditional optimization methods work very well in small or static systems but are suffer difficulty on large-scale, dynamic and stochastic renewable environment due to their NP-hard nature. Methods The framework introduces the concept of a Machine Learning-Assisted Hybrid Cuckoo Search (ML-HCS) that combines CS with a hybrid metaheuristic and integrates Long Short-Term Memory (LSTM) networks for forecasting based on both regression models of LSTMs and hybrid optimization algorithm

Breast cancer is highlighted in recent research as one of the most prevalent types of cancer. Timely identification is essential for enhancing patient results and decreasing fatality rates. Utilizing computer-assisted detection and diagnosis early on may greatly improve the chances of recovery by accurately predicting outcomes and developing suitable treatment plans. Grading breast cancer properly, especially evaluating nuclear atypia, is difficult owing to faults and inconsistencies in slide preparation and the intricate nature of tissue patterns. This work explores the capability of deep learning to extract characteristics from histopathology photos of breast cancer. The research introduces a new method called SMOTE-based Convolut

Objectives: To assess the qualifications and performance of newly graduated pharmacists and explore factors that affect their performance in public hospitals. Methods: This qualitative study included face-to-face, semi-structured interviews with hospital practitioner pharmacists. It was conducted between March and May 2023 in Kerbala province, Iraq. The participating pharmacists were selected purposefully because they had three or more years of work experience at governmental hospitals. The audio-recording interviews were transcribed. Thematic analyses were used to generate themes and subthemes from the interviews. Results: Twenty-seven hospital pharmacists participated in this study. The study found that there are several factors t

The geological modeling of an oil-bearing reservoir, which specifies the lithology and analyzes the petrophysical parameters of complex reservoir, its being created using Petrel software to construct reservoir models that describe and estimate porosity and water saturation distributions. 3D modeling of carbonate reservoirs has proven to be a highly effective technique for evaluate the economic benefit of the reservoir by estimating the formation petrophysical properties and calculating the oil reserves. The study aims constructed on evaluated Yamama formation in Four drilling wells (Nasiriyah-1, Nasiriyah-3, Nasiriyah-4, and Nasiriyah-5) entering the Yamama Formation of the Al-Nasiriyah field. In Al-Nasiriyah Oil field, and within t

New Schiff base ligand (E)-6-(2-(4-(dimethylamino)benzylideneamino)-2-(4-hydroxyphenyl)acetamido)-3,3- dimethyl-7-oxo-4-thia-1- azabicyclo[3.2.0]heptane-2-carboxylic acid = (HL) was synthesized via condensation of Amoxicillin and 4(dimethylamino)benzaldehyde in methanol. Figure -1 Polydentate mixed ligand complexes were obtained from 1:1:2 molar ratio reactions with metal ions and HL, 2NA on reaction with MCl2 .nH2O salt yields complexes corresponding to the formulas [M(L)(NA)2Cl],where M=Fe(II),Co(II),Ni(II),Cu(II),and Zn(II), A=nicotinamide .